Attention-based LSTM for Aspect-level Sentiment Classification

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 606–615, Austin, Texas, November 1-5, 2016. c⃝2016 Association for Computational Linguistics Attention-based LSTM for Aspect-level Sentiment Classification Yequan Wang and Minlie Huang and Li Zhao* and Xiaoyan Zhu State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China *Microsoft Research Asia wangyequan@live.cn, aihuang@tsinghua.edu.cn lizo@microsoft.com, zxy-dcs@tsinghua.edu.cn Abstract Aspect-level sentiment classification is a fine- grained task in sentiment analysis. Since it provides more complete and in-depth results, aspect-level sentiment analysis has received much attention these years. In this paper, we reveal that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect. For instance, “The appetizers are ok, but the service is slow.”, for aspect taste, the polar- ity is positive while for service, the polarity is negative. Therefore, it is worthwhile to ex- plore the connection between an aspect and the content of a sentence. To this end, we propose an Attention-based Long Short-Term Memory Network for aspect-level sentiment classification. The attention mechanism can concentrate on different parts of a sentence when different aspects are taken as input. We experiment on the SemEval 2014 dataset and results show that our model achieves state-of- the-art performance on aspect-level sentiment classification. 1 Introduction Sentiment analysis (Nasukawa and Yi, 2003), also known as opinion mining (Liu, 2012), is a key NLP task that receives much attention these years. Aspect-level sentiment analysis is a fine-grained task that can provide complete and in-depth results. In this paper, we deal with aspect-level sentiment classification and we find that the sentiment polar- ity of a sentence is highly dependent on both con- tent and aspect. For example, the sentiment polarity of “Staffs are not that friendly, but the taste covers all.” will be positive if the aspect is food but neg- ative when considering the aspect service. Polarity could be opposite when different aspects are consid- ered. Neural networks have achieved state-of-the-art performance in a variety of NLP tasks such as ma- chine translation (Lample et al., 2016), paraphrase identification (Yin et al., 2015), question answer- ing (Golub and He, 2016) and text summariza- tion (Rush et al., 2015). However, neural net- work models are still in infancy to deal with aspect- level sentiment classification. In some works, tar- get dependent sentiment classification can be ben- efited from taking into account target information, such as in Target-Dependent LSTM (TD-LSTM) and Target-Connection LSTM (TC-LSTM) (Tang et al., 2015a). However, those models can only take into consideration the target but not aspect informa- tion which is proved to be crucial for aspect-level classification. Attention has become an effective mechanism to obtain superior results, as demonstrated in image recognition (Mnih et al., 2014), machine transla- tion (Bahdanau et al., 2014), reasoning about entail- ment (Rockt¨aschel et al., 2015) and sentence sum- marization (Rush et al., 2015). Even more, neural …

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